Knowledge workers share two basic needs: their productivity requires isolation (internalization of knowledge) and interaction (externalization of knowledge), supported by different spatial concepts. None of the work environments involved in the study adequately support all of the phases in the knowledge development process adequately. Collective productivity is primarily determined by the physical dimension of the workplace; whereas the social dimension is crucial for personal productivity. Social interaction has a stronger effect than distraction; and the layout has a stronger effect than comfort.
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This paper explores the impact of the physical and social dimensions of the work environment on satisfaction and perceived productivity of knowledge workers in Dutch universities of applied sciences. The approach took the form of a literature review, multiple case study of six research centres using interviews and logbook analysis, and web-based survey (N = 188). Optimally facilitating knowledge production requires both space for concentration (to support internalisation of knowledge) and space for interaction (to support externalisation of knowledge). None of the work environments involved in the study adequately supported all the phases of knowledge development adequately. Cellular offices with personal desks are preferred for solo work and, whereas new workplace designs with a focus on the office as a meeting place support interaction and collaboration. Spatial layout and interaction have a stronger impact than comfort and absence of distraction. The spatial layout should support both in-depth concentration and communication, fit the internalisation/externalisation ratio of activities, and accommodate the proximity essential for collaborative knowledge development. Being able to choose is the key to success. In terms of research limitations, knowledge workers’ productivity was measured by self-assessment, but only a limited number of diaries were collected. The lessons learned can be used as inputs to decision-making processes regarding the design, implementation and management of working environments in higher education settings. Few studies have been conducted concerning the spatial preferences and needs of knowledge workers in universities of applied sciences. The results show that the physical dimension (comfort and layout) is more important for collective productivity, whereas individual productivity is more strongly influenced by the social dimension (interaction and distraction).
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This paper explores the impact of the physical and social dimensions of the work environment on satisfaction and perceived productivity of knowledge workers in Dutch universities of applied sciences. The approach took the form of a literature review, multiple case study of six research centres using interviews and logbook analysis, and web-based survey (N = 188). Optimally facilitating knowledge production requires both space for concentration (to support internalisation of knowledge) and space for interaction (to support externalisation of knowledge). None of the work environments involved in the study adequately supported all the phases of knowledge development adequately. Cellular offices with personal desks are preferred for solo work and, whereas new workplace designs with a focus on the office as a meeting place support interaction and collaboration. Spatial layout and interaction have a stronger impact than comfort and absence of distraction. The spatial layout should support both in-depth concentration and communication, fit the internalisation/externalization ratio of activities, and accommodate the proximity essential for collaborative knowledge development. Being able to choose is the key to success. In terms of research limitations, knowledge workers’ productivity was measured by self-assessment, but only a limited number of diaries were collected. The lessons learned can be used as inputs to decision-making processes regarding the design, implementation and management of workingenvironments in higher education settings. Few studies have been conducted concerning the spatial preferences and needs of knowledge workers in universities of applied sciences. The results show that the physical dimension (comfort and layout) is more important for collective productivity, whereas individual productivity is more strongly influenced by the social dimension (interaction and distraction).
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Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low. As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers. To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is: How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems? HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases: I. Highway: non-professional drivers; II. Distribution Centre: professional drivers.
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low.As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers.To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is:How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems?HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases:I. Highway: non-professional drivers;II. Distribution Centre: professional drivers.Collaborative partners:Bielefeld University of Applied Sciences, Bricklog B.V., Goudappel B.V., HaskoningDHV Nederland B.V., Rhine-Waal University of Applied Sciences, Rijkswaterstaat, Saxion, Sencure B.V., Siemens Industry Software Netherlands B.V., Smits Opleidingen B.V., Stichting Innovatiecentrum Verkeer en Logistiek, TNO Den Haag, TU Delft, University of Twente, V-Tron B.V., XL Businesspark Twente.
